Nested Named Entity Recognition as Building Local Hypergraphs
Authors: Yukun Yan, Bingling Cai, Sen Song
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments illustrate that our model outperforms previous state-of-the-art methods on four widely used nested named entity recognition datasets: ACE04, ACE05, GENIA, and KBP17. |
| Researcher Affiliation | Academia | Yukun Yan,1,2 Bingling Cai,1,2 Sen Song1,2* 1Biomedical Department, Tsinghua University 2Laboratory of Brain and Intelligence, Tsinghua University {yanyk13, caibl13}@mails.tsinghua.edu.cn, songsen@tsinghua.edu.cn |
| Pseudocode | No | The paper describes steps and rules within the text and using equations (e.g., rules R for hypergraph building), but it does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | The code is available at https://github.com/yanyk13/local-hypergraphbuilding-network.git. |
| Open Datasets | Yes | To evaluate the proposed method, we conduct experiments on four widely used datasets for Nested NER: ACE04, ACE05, KBP17 and GENIA. ACE04 and ACE05(Doddington et al. 2004; Stephanie Strassel and Maeda 2006) are nested datasets with 7 entity categories, we use the same setup as previous works(Katiyar and Cardie 2018; Shen et al. 2021) and split them into train, dev, and test sets by 8:1:1. GENIA(Ohta et al. 2002) is a nested dataset consisting of biology texts. There are 5 entity types: DNA, RNA, protein, cell line and cell categories. Following (Shen et al. 2021), we use a 90%/10% train/test split. KBP17(Ji et al. 2017) has 5 entity categories. We split all the samples into 866/20/167 documents for train/dev/test set following the same setup as previous works(Shen et al. 2021). |
| Dataset Splits | Yes | ACE04 and ACE05...we use the same setup as previous works(Katiyar and Cardie 2018; Shen et al. 2021) and split them into train, dev, and test sets by 8:1:1. GENIA...we use a 90%/10% train/test split. KBP17...We split all the samples into 866/20/167 documents for train/dev/test set following the same setup as previous works(Shen et al. 2021). |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. It only mentions using BERT-large and Bio BERT-large models, which implies computational resources but without specific hardware identification. |
| Software Dependencies | No | The paper mentions specific tools and models like BERT-large, GloVe, Bio BERT-large, Bio Wordvec, and AdamW optimizer, but it does not provide specific version numbers for these or other software dependencies like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | Based on the performance on the dev sets of ACE04, ACE05, and KBP17, γ used in equation (6) is set to 0.9, the scale hyper-parameter λ for sampling boundary candidates is set to 5, and the merging threshold θ is set to 0.5. For all the experiments, we train our model for 100 epochs with an Adam W optimizer and a linear warmup-decay learning rate. The initial learning rate for BERT modules and other parameters are set to 1e-5, and 1e-3 respectively. |